mlsanityData to 7 Oct 2026

Synaptic Plasticity Models and Bio-Inspired Unsupervised Deep Learning: A Survey

arXiv 2307.16236 ↗First posted 30 Jul 2023cs.NE cs.AI cs.CVcited by 7

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Abstract (arXiv)

Recently emerged technologies based on Deep Learning (DL) achieved outstanding results on a variety of tasks in the field of Artificial Intelligence (AI). However, these encounter several challenges related to robustness to adversarial inputs, ecological impact, and the necessity of huge amounts of training data. In response, researchers are focusing more and more interest on biologically grounded mechanisms, which are appealing due to the impressive capabilities exhibited by biological brains. This survey explores a range of these biologically inspired models of synaptic plasticity, their application in DL scenarios, and the connections with models of plasticity in Spiking Neural Networks (SNNs). Overall, Bio-Inspired Deep Learning (BIDL) represents an exciting research direction, aiming at advancing not only our current technologies but also our understanding of intelligence.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 64 of its 256 references that are in the map. First posted on arXiv 30 Jul 2023.

Builds on

64 of 256 references.

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50 most cited of 64 All 64 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 4; 2026: 1.

7 papers.

  1. 2024Biologically-inspired Semi-supervised Semantic Segmentation for Biomedical Imagingcited by 4
  2. 2025Advancing the Biological Plausibility and Efficacy of Hebbian Convolutional Neural Networkscited by 4
  3. 2025From Neurons to Computation: Biological Reservoir Computing for Pattern Recognitioncited by 2
  4. 2024Unsupervised End-to-End Training with a Self-Defined Targetcited by 1
  5. 2025From Neural Activity to Computation: Biological Reservoirs for Pattern Recognition in Digit Classificationcited by 1
  6. 2026Neuro-Inspired Visual Pattern Recognition via Biological Reservoir Computingcited by 1
  7. 2025CA3D: Convolutional-Attentional 3D Nets for Efficient Video Activity Recognition on the Edgecited by 0